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PrivPRISM: Google Playのデータ安全性宣言と開発者プライバシーポリシー間の不一致を自動検出する

arXiv cs.AI / 2026/3/11

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要点

  • PrivPRISMは、エンコーダーおよびデコーダー言語モデルを用いて、アプリのプライバシーポリシーから詳細なデータ取扱いを自動抽出し、Google Playが要求する簡易化データ安全性宣言と比較する新しいフレームワークです。
  • 7,770の人気モバイルゲームを調査した結果、約53%のアプリで不一致が見つかり、さらに広く使われる一般的なアプリ1,711件では61%に上ることが判明し、ユーザーを誤解させる不一致がはびこり、規制要件に違反していることが明らかになりました。
  • 静的コード解析により、センシティブなデータアクセスの大幅な過小申告も判明。プライバシーポリシーは潜在的なセンシティブデータ利用のうち66.8%のみ開示し、データ安全性宣言はわずか36.4%のみを開示していました。
  • 本研究は、汎用プライバシーポリシーの流用、曖昧または矛盾する記述、1億回以上ダウンロードされている著名アプリに潜むリスクなど、システム的な問題を浮き彫りにしています。
  • これらの結果は、PrivPRISMのような自動化された監査ツールによる迅速な規制強化の必要性を示すとともに、エンドユーザーがモバイルアプリに提供するセンシティブデータに対して注意を払うことの重要性を強調しています。

Computer Science > Artificial Intelligence

arXiv:2603.09214 (cs)
[Submitted on 10 Mar 2026]

Title:PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies

View a PDF of the paper titled PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies, by Bhanuka Silva and 4 other authors
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Abstract:End-users seldom read verbose privacy policies, leading app stores like Google Play to mandate simplified data safety declarations as a user-friendly alternative. However, these self-declared disclosures often contradict the full privacy policies, deceiving users about actual data practices and violating regulatory requirements for consistency. To address this, we introduce PrivPRISM, a robust framework that combines encoder and decoder language models to systematically extract and compare fine-grained data practices from privacy policies and to compare against data safety declarations, enabling scalable detection of non-compliance. Evaluating 7,770 popular mobile games uncovers discrepancies in nearly 53% of cases, rising to 61% among 1,711 widely used generic apps. Additionally, static code analysis reveals possible under-disclosures, with privacy policies disclosing just 66.8% of potential accesses to sensitive data like location and financial information, versus only 36.4% in data safety declarations of mobile games. Our findings expose systemic issues, including widespread reuse of generic privacy policies, vague / contradictory statements, and hidden risks in high-profile apps with 100M+ downloads, underscoring the urgent need for automated enforcement to protect platform integrity and for end-users to be vigilant about sensitive data they disclose via popular apps.
Comments:
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.09214 [cs.AI]
  (or arXiv:2603.09214v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2603.09214
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arXiv-issued DOI via DataCite

Submission history

From: Bhanuka Pinchahewage Malith Silva [view email]
[v1] Tue, 10 Mar 2026 05:33:40 UTC (2,718 KB)
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